gastro_data

<p>The collected dataset is composed of 76 colonoscopic videos (recorded with both White Light (WL) and Narrow Band Imaging (NBI)), the histology (classification ground truth), and  the endoscopist's opinion (including 4 experts and 3 beginners). There are $n=76$ observations, and each observation consists of 698 features extracted from colonoscopic videos on patients with gastrointestinal lesions.</p>

The algorithm of semi-supervised learning is based on finite Gaussian mixture models and includes a mechanism for handling missing data. It aims to fit a g-class Gaussian mixture model using maximum likelihood. The algorithm treats the labels of unclassified features as missing data, building on the framework introduced by Rubin (1976) <doi:10.2307/2335739> for missing data analysis. By taking into account the dependencies in the missing pattern, the algorithm provides more information for determining the optimal classifier, as specified by Bayes' rule.

Ziyang Lyu

gmmsslm

Semi-Supervised Gaussian Mixture Model with a Missing-Data
Mechanism

Ziyang Lyu 

Daniel Ahfock 

Ryan Thompson 

Geoffrey J. McLachlan 

gastro_data function

Gastrointestinal dataset — gastro_data

Gastrointestinal dataset

gastro_data: Gastrointestinal dataset

Description

Arguments

References